用机器学习预测聚合物阻燃性能，加速安全材料研发。
A machine learning platform for development of low flammability polymers
- 结合合成数据与分子描述符，构建聚合物阻燃性预测模型。
- 在有限实验数据下仍实现对最大放热率等指标的准确预测。
- 集成至MatVerse平台，提供网页端交互式设计工具。
阻燃性指数（FI）及锥形量热仪测试结果（如最大放热率、点火时间、总烟释放量和火灾增长速率）是评估聚合物防火安全的关键指标。由于材料受热行为复杂，准确预测这些性能极具挑战。本文研究了机器学习技术在预测上述阻燃指标中的应用。通过Synthetic Data Vault生成合成聚合物以扩充实验数据集，并结合自研分子描述符与RDkit库生成的特征进行建模。尽管实验数据有限，模型仍展现出对FI及锥形量热仪结果的高精度预测能力，为设计更安全聚合物提供支持。此外，我们开发了POLYCOMPRED模块，集成于云平台MatVerse，提供可访问的Web界面用于阻燃性能预测。本工作不仅实现了聚合物阻燃性的预测建模，还提供交互式分析工具，助力具有定制防火性能新材料的发现与设计。
原文摘要 · Abstract (English)
Flammability index (FI) and cone calorimetry outcomes, such as maximum heat release rate, time to ignition, total smoke release, and fire growth rate, are critical factors in evaluating the fire safety of polymers. However, predicting these properties is challenging due to the complexity of material behavior under heat exposure. In this work, we investigate the use of machine learning (ML) techniques to predict these flammability metrics. We generated synthetic polymers using Synthetic Data Vault to augment the experimental dataset. Our comprehensive ML investigation employed both our polymer descriptors and those generated by the RDkit library. Despite the challenges of limited experimental data, our models demonstrate the potential to accurately predict FI and cone calorimetry outcomes, which could be instrumental in designing safer polymers. Additionally, we developed POLYCOMPRED, a module integrated into the cloud-based MatVerse platform, providing an accessible, web-based interface for flammability prediction. This work provides not only the predictive modeling of polymer flammability but also an interactive analysis tool for the discovery and design of new materials with tailored fire-resistant properties.
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